AI Agents in the Wild: Building, Scaling & Deploying in Production

Join us for a high-signal evening with senior leaders from GitHub, Salesforce, and AWS who are at the forefront of shipping and scaling production-grade AI agents.

📅 Date: August 12th
📍 Location: Bellevue City Hall

### 🗓️ Agenda

4:30 PM – 5:30 PM
Check-in & Networking
5:30 PM – 6:00 PM
Matt Nigh – Program Manager Director, AI (GitHub)
Topic: TBA
6:00 PM – 6:30 PM
Ran Fu – Director of AI Agent Product Management, Salesforce
Topic: Post Day 1: How to Optimize Agents with Evals and Session Traces, After Building Them from 0 to 1
6:30 PM – 7:00 PM
Raj Ganesh Jayaraman – Senior GenAI Solutions Architect, AWS
Topic: AI Agents in Production and Its Challenges
7:00 PM – 8:00 PM
Networking

### Why Attend

• Most teams have built an agent demo. Very few have one running reliably in production. This event is for those closing that gap.
• Understand what evaluation frameworks actually look like in practice — not just theory, but the traces, metrics, and iteration loops that drive real agent improvement.
• Go beyond the initial build and learn why optimization, observability, and session tracing are the real differentiators once agents are live.
• Discover how leading teams at GitHub, Salesforce, and AWS are solving for scalability, reliability, and cost in production AI agent deployments.
• Learn how developer workflows are being reshaped by agentic systems — and what that means for teams building on or alongside platforms like GitHub.
• Get a clear view of where most agent projects stall — not at the model level, but at deployment, evaluation, and architectural decision points.

### Who Should Attend

• Professionals building, deploying, or scaling AI agents and agentic workflows
• Teams exploring production-ready AI systems across GitHub, Salesforce, and AWS ecosystems
• Individuals interested in observability, tracing, monitoring, and evaluation frameworks for AI applications
• Practitioners moving AI solutions from prototype to enterprise production environments
• Organizations looking to improve the reliability, scalability, and performance of AI-driven systems
• Builders and innovators exploring real-world use cases for autonomous and intelligent AI applications

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